Overview

Dataset statistics

Number of variables11
Number of observations626
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)0.2%
Total size in memory53.9 KiB
Average record size in memory88.2 B

Variable types

Numeric11

Alerts

Dataset has 1 (0.2%) duplicate rowsDuplicates
Ages[15-49]_All_grade1 is highly overall correlated with Ages[15-49]_All_grade2 and 7 other fieldsHigh correlation
Ages[15-49]_All_grade2 is highly overall correlated with Ages[15-49]_All_grade1 and 7 other fieldsHigh correlation
Ages[15-49]_All_grade3 is highly overall correlated with Ages[15-49]_All_grade1 and 7 other fieldsHigh correlation
Ages[15-49]_Male_grade1 is highly overall correlated with Ages[15-49]_All_grade1 and 7 other fieldsHigh correlation
Ages[15-49]_Male_grade2 is highly overall correlated with Ages[15-49]_All_grade1 and 7 other fieldsHigh correlation
Ages[15-49]_Male_grade3 is highly overall correlated with Ages[15-49]_All_grade1 and 7 other fieldsHigh correlation
Ages[15-49]_Female_grade1 is highly overall correlated with Ages[15-49]_All_grade1 and 7 other fieldsHigh correlation
Ages[15-49]_Female_grade2 is highly overall correlated with Ages[15-49]_All_grade1 and 7 other fieldsHigh correlation
Ages[15-49]_Female_grade3 is highly overall correlated with Ages[15-49]_All_grade1 and 7 other fieldsHigh correlation

Reproduction

Analysis started2023-11-05 00:22:36.766691
Analysis finished2023-11-05 00:22:57.314574
Duration20.55 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

Ages[15-49]_All_grade1
Real number (ℝ)

HIGH CORRELATION 

Distinct620
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.87388596
Minimum0.25191653
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-11-05T01:22:57.457631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.25191653
5-th percentile0.49381156
Q10.83179116
median0.94892552
Q30.98444663
95-th percentile0.99602222
Maximum1
Range0.74808347
Interquartile range (IQR)0.15265547

Descriptive statistics

Standard deviation0.1626244
Coefficient of variation (CV)0.18609339
Kurtosis2.8206217
Mean0.87388596
Median Absolute Deviation (MAD)0.044235229
Skewness-1.8131651
Sum547.05261
Variance0.026446696
MonotonicityNot monotonic
2023-11-05T01:22:57.673131image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5
 
0.8%
0.9897984862 2
 
0.3%
0.8710972071 2
 
0.3%
0.669090867 1
 
0.2%
0.7619048953 1
 
0.2%
0.9380534291 1
 
0.2%
0.9654742479 1
 
0.2%
0.6645562053 1
 
0.2%
0.5785804987 1
 
0.2%
0.7300003767 1
 
0.2%
Other values (610) 610
97.4%
ValueCountFrequency (%)
0.2519165277 1
0.2%
0.2675411105 1
0.2%
0.2743234634 1
0.2%
0.2803148031 1
0.2%
0.2880326807 1
0.2%
0.2919573486 1
0.2%
0.2934274077 1
0.2%
0.294970125 1
0.2%
0.3171576262 1
0.2%
0.3236398697 1
0.2%
ValueCountFrequency (%)
1 5
0.8%
0.9998298287 1
 
0.2%
0.9997598529 1
 
0.2%
0.9992375374 1
 
0.2%
0.998536706 1
 
0.2%
0.9983882904 1
 
0.2%
0.9981418252 1
 
0.2%
0.9980663657 1
 
0.2%
0.9980211854 1
 
0.2%
0.9979032874 1
 
0.2%

Ages[15-49]_All_grade2
Real number (ℝ)

HIGH CORRELATION 

Distinct624
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86149013
Minimum0.24407127
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-11-05T01:22:57.876127image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.24407127
5-th percentile0.48048056
Q10.80313134
median0.93516687
Q30.98011227
95-th percentile0.99446787
Maximum1
Range0.75592873
Interquartile range (IQR)0.17698093

Descriptive statistics

Standard deviation0.16689506
Coefficient of variation (CV)0.19372835
Kurtosis2.3483078
Mean0.86149013
Median Absolute Deviation (MAD)0.055145472
Skewness-1.6846941
Sum539.29282
Variance0.027853961
MonotonicityNot monotonic
2023-11-05T01:22:58.093358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9825596809 2
 
0.3%
0.8423897028 2
 
0.3%
0.667214036 1
 
0.2%
0.7491188645 1
 
0.2%
0.9337501526 1
 
0.2%
0.9609167576 1
 
0.2%
0.6553105712 1
 
0.2%
0.5732329488 1
 
0.2%
0.7211663723 1
 
0.2%
0.7528111339 1
 
0.2%
Other values (614) 614
98.1%
ValueCountFrequency (%)
0.244071275 1
0.2%
0.2617967129 1
0.2%
0.26510185 1
0.2%
0.2713519633 1
0.2%
0.2782647014 1
0.2%
0.2859278917 1
0.2%
0.2865097225 1
0.2%
0.2880326807 1
0.2%
0.3047781587 1
0.2%
0.3124300241 1
0.2%
ValueCountFrequency (%)
1 1
0.2%
0.9997598529 1
0.2%
0.9995408654 1
0.2%
0.9992375374 1
0.2%
0.9991862774 1
0.2%
0.9991492033 1
0.2%
0.9983882904 1
0.2%
0.9981418252 1
0.2%
0.9980663657 1
0.2%
0.997951448 1
0.2%

Ages[15-49]_All_grade3
Real number (ℝ)

HIGH CORRELATION 

Distinct624
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83750852
Minimum0.23327357
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-11-05T01:22:58.315906image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.23327357
5-th percentile0.45065869
Q10.7574646
median0.90958688
Q30.97116649
95-th percentile0.99344964
Maximum1
Range0.76672643
Interquartile range (IQR)0.21370189

Descriptive statistics

Standard deviation0.17655454
Coefficient of variation (CV)0.21080925
Kurtosis1.4854223
Mean0.83750852
Median Absolute Deviation (MAD)0.074039847
Skewness-1.4410209
Sum524.28033
Variance0.031171507
MonotonicityNot monotonic
2023-11-05T01:22:58.530432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9743223786 2
 
0.3%
0.7827367187 2
 
0.3%
0.6518120766 1
 
0.2%
0.7309865952 1
 
0.2%
0.9246086478 1
 
0.2%
0.9508134723 1
 
0.2%
0.6347177029 1
 
0.2%
0.5533608794 1
 
0.2%
0.7002702951 1
 
0.2%
0.7337198257 1
 
0.2%
Other values (614) 614
98.1%
ValueCountFrequency (%)
0.2332735658 1
0.2%
0.2465851754 1
0.2%
0.2524287999 1
0.2%
0.2550808787 1
0.2%
0.2564779818 1
0.2%
0.2600584328 1
0.2%
0.2625986636 1
0.2%
0.2767798603 1
0.2%
0.2866811752 1
0.2%
0.2912916541 1
0.2%
ValueCountFrequency (%)
1 1
0.2%
0.9997598529 1
0.2%
0.9995408654 1
0.2%
0.9992375374 1
0.2%
0.9991862774 1
0.2%
0.9981418252 1
0.2%
0.9980663657 1
0.2%
0.997951448 1
0.2%
0.9974088669 1
0.2%
0.9973631501 1
0.2%

Ages[15-49]_Male_grade1
Real number (ℝ)

HIGH CORRELATION 

Distinct616
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.89462837
Minimum0.3170841
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-11-05T01:22:58.757892image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.3170841
5-th percentile0.60378711
Q10.86819384
median0.95100284
Q30.98532873
95-th percentile0.99570283
Maximum1
Range0.6829159
Interquartile range (IQR)0.1171349

Descriptive statistics

Standard deviation0.13670619
Coefficient of variation (CV)0.1528078
Kurtosis4.0727706
Mean0.89462837
Median Absolute Deviation (MAD)0.040918827
Skewness-2.0379172
Sum560.03736
Variance0.018688583
MonotonicityNot monotonic
2023-11-05T01:22:59.119079image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8
 
1.3%
0.9883913994 2
 
0.3%
0.974191308 2
 
0.3%
0.9193992615 2
 
0.3%
0.8165464997 1
 
0.2%
0.7105631232 1
 
0.2%
0.9001092911 1
 
0.2%
0.9613373876 1
 
0.2%
0.9636002183 1
 
0.2%
0.9727343917 1
 
0.2%
Other values (606) 606
96.8%
ValueCountFrequency (%)
0.3170841038 1
0.2%
0.3227265477 1
0.2%
0.3361186683 1
0.2%
0.3570645154 1
0.2%
0.3645169735 1
0.2%
0.3852404356 1
0.2%
0.3878842592 1
0.2%
0.3884650469 1
0.2%
0.3926050365 1
0.2%
0.3927299678 1
0.2%
ValueCountFrequency (%)
1 8
1.3%
0.9995279908 1
 
0.2%
0.9993175268 1
 
0.2%
0.9992207289 1
 
0.2%
0.9985312819 1
 
0.2%
0.998318851 1
 
0.2%
0.9982489347 1
 
0.2%
0.9981643558 1
 
0.2%
0.9974358678 1
 
0.2%
0.9971903563 1
 
0.2%

Ages[15-49]_Male_grade2
Real number (ℝ)

HIGH CORRELATION 

Distinct619
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88146145
Minimum0.31467757
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-11-05T01:22:59.341553image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.31467757
5-th percentile0.57457919
Q10.83308941
median0.9371483
Q30.98004535
95-th percentile0.99373174
Maximum1
Range0.68532243
Interquartile range (IQR)0.14695594

Descriptive statistics

Standard deviation0.14154843
Coefficient of variation (CV)0.1605838
Kurtosis3.3409233
Mean0.88146145
Median Absolute Deviation (MAD)0.05106622
Skewness-1.8611179
Sum551.79486
Variance0.020035957
MonotonicityNot monotonic
2023-11-05T01:22:59.554704image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4
 
0.6%
0.899446547 2
 
0.3%
0.9682109952 2
 
0.3%
0.9937888384 2
 
0.3%
0.9804298282 2
 
0.3%
0.7886363268 1
 
0.2%
0.922313869 1
 
0.2%
0.787109673 1
 
0.2%
0.8124693632 1
 
0.2%
0.8105757236 1
 
0.2%
Other values (609) 609
97.3%
ValueCountFrequency (%)
0.3146775663 1
0.2%
0.3158782125 1
0.2%
0.3224535882 1
0.2%
0.3381987214 1
0.2%
0.3433669806 1
0.2%
0.344794482 1
0.2%
0.3772052228 1
0.2%
0.3820918202 1
0.2%
0.383697927 1
0.2%
0.3839564621 1
0.2%
ValueCountFrequency (%)
1 4
0.6%
0.9995279908 1
 
0.2%
0.9992207289 1
 
0.2%
0.9990741014 1
 
0.2%
0.9990300536 1
 
0.2%
0.9987632632 1
 
0.2%
0.9985312819 1
 
0.2%
0.998318851 1
 
0.2%
0.9982489347 1
 
0.2%
0.9971903563 1
 
0.2%

Ages[15-49]_Male_grade3
Real number (ℝ)

HIGH CORRELATION 

Distinct621
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.85598732
Minimum0.27713883
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-11-05T01:22:59.790401image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.27713883
5-th percentile0.52692336
Q10.78992842
median0.91052905
Q30.97107577
95-th percentile0.99295688
Maximum1
Range0.72286117
Interquartile range (IQR)0.18114735

Descriptive statistics

Standard deviation0.15242655
Coefficient of variation (CV)0.17807103
Kurtosis2.0777243
Mean0.85598732
Median Absolute Deviation (MAD)0.070990086
Skewness-1.5383101
Sum535.84806
Variance0.023233852
MonotonicityNot monotonic
2023-11-05T01:23:00.004394image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3
 
0.5%
0.840878427 2
 
0.3%
0.9682106972 2
 
0.3%
0.9533179998 2
 
0.3%
0.7796168327 1
 
0.2%
0.8659395576 1
 
0.2%
0.9381895065 1
 
0.2%
0.9500974417 1
 
0.2%
0.9597895145 1
 
0.2%
0.748916328 1
 
0.2%
Other values (611) 611
97.6%
ValueCountFrequency (%)
0.2771388292 1
0.2%
0.3006979227 1
0.2%
0.3042455316 1
0.2%
0.3064506352 1
0.2%
0.3200441301 1
0.2%
0.3216159344 1
0.2%
0.3519760668 1
0.2%
0.3533827066 1
0.2%
0.3576131165 1
0.2%
0.3614393771 1
0.2%
ValueCountFrequency (%)
1 3
0.5%
0.9995279908 1
 
0.2%
0.9992207289 1
 
0.2%
0.9990741014 1
 
0.2%
0.9985312819 1
 
0.2%
0.998318851 1
 
0.2%
0.9982489347 1
 
0.2%
0.9971903563 1
 
0.2%
0.9967224002 1
 
0.2%
0.996450901 1
 
0.2%

Ages[15-49]_Female_grade1
Real number (ℝ)

HIGH CORRELATION 

Distinct615
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.85350971
Minimum0.16565166
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-11-05T01:23:00.217151image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.16565166
5-th percentile0.40534711
Q10.79870789
median0.95075852
Q30.98578626
95-th percentile0.99780954
Maximum1
Range0.83434834
Interquartile range (IQR)0.18707837

Descriptive statistics

Standard deviation0.19201687
Coefficient of variation (CV)0.22497327
Kurtosis2.0137774
Mean0.85350971
Median Absolute Deviation (MAD)0.045154572
Skewness-1.6665726
Sum534.29708
Variance0.036870478
MonotonicityNot monotonic
2023-11-05T01:23:00.439497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10
 
1.6%
0.9912207127 2
 
0.3%
0.8230339885 2
 
0.3%
0.5127329826 1
 
0.2%
0.8611063361 1
 
0.2%
0.9133958817 1
 
0.2%
0.9590655565 1
 
0.2%
0.5459342599 1
 
0.2%
0.4349412918 1
 
0.2%
0.6482753158 1
 
0.2%
Other values (605) 605
96.6%
ValueCountFrequency (%)
0.1656516641 1
0.2%
0.1808767766 1
0.2%
0.1944844425 1
0.2%
0.2062006891 1
0.2%
0.2071643323 1
0.2%
0.2126361877 1
0.2%
0.219355002 1
0.2%
0.2283948809 1
0.2%
0.2371551096 1
0.2%
0.2412475795 1
0.2%
ValueCountFrequency (%)
1 10
1.6%
0.9999153018 1
 
0.2%
0.9996408224 1
 
0.2%
0.9995087981 1
 
0.2%
0.9992818236 1
 
0.2%
0.999263823 1
 
0.2%
0.9992578626 1
 
0.2%
0.9991359711 1
 
0.2%
0.9990851283 1
 
0.2%
0.9988521934 1
 
0.2%

Ages[15-49]_Female_grade2
Real number (ℝ)

HIGH CORRELATION 

Distinct619
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.841888
Minimum0.16565166
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-11-05T01:23:00.674411image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.16565166
5-th percentile0.38881612
Q10.77708541
median0.93913272
Q30.98259944
95-th percentile0.99663724
Maximum1
Range0.83434834
Interquartile range (IQR)0.20551403

Descriptive statistics

Standard deviation0.19602211
Coefficient of variation (CV)0.23283633
Kurtosis1.6757311
Mean0.841888
Median Absolute Deviation (MAD)0.054734707
Skewness-1.5660127
Sum527.02189
Variance0.038424669
MonotonicityNot monotonic
2023-11-05T01:23:00.888605image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6
 
1.0%
0.9847123623 2
 
0.3%
0.785615027 2
 
0.3%
0.5111901164 1
 
0.2%
0.8543340564 1
 
0.2%
0.9095195532 1
 
0.2%
0.9537050128 1
 
0.2%
0.537786901 1
 
0.2%
0.4316311479 1
 
0.2%
0.6409156322 1
 
0.2%
Other values (609) 609
97.3%
ValueCountFrequency (%)
0.1656516641 1
0.2%
0.1720313728 1
0.2%
0.1885119081 1
0.2%
0.2012457699 1
0.2%
0.2018099427 1
0.2%
0.2020214796 1
0.2%
0.2145776004 1
0.2%
0.2168838978 1
0.2%
0.2342349887 1
0.2%
0.2346676439 1
0.2%
ValueCountFrequency (%)
1 6
1.0%
0.9994249344 1
 
0.2%
0.9993582368 1
 
0.2%
0.9992818236 1
 
0.2%
0.9992815852 1
 
0.2%
0.999263823 1
 
0.2%
0.9992578626 1
 
0.2%
0.9991359711 1
 
0.2%
0.9990851283 1
 
0.2%
0.9990175962 1
 
0.2%

Ages[15-49]_Female_grade3
Real number (ℝ)

HIGH CORRELATION 

Distinct620
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81941753
Minimum0.15985909
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-11-05T01:23:01.085867image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.15985909
5-th percentile0.3682645
Q10.72535779
median0.91109282
Q30.97508468
95-th percentile0.99613343
Maximum1
Range0.84014091
Interquartile range (IQR)0.24972689

Descriptive statistics

Standard deviation0.2049551
Coefficient of variation (CV)0.25012291
Kurtosis1.0501032
Mean0.81941753
Median Absolute Deviation (MAD)0.077404797
Skewness-1.3748438
Sum512.95538
Variance0.042006591
MonotonicityNot monotonic
2023-11-05T01:23:01.283646image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5
 
0.8%
0.9804996252 2
 
0.3%
0.7248824239 2
 
0.3%
0.4934135377 1
 
0.2%
0.8298110962 1
 
0.2%
0.9000070691 1
 
0.2%
0.9428899884 1
 
0.2%
0.521871984 1
 
0.2%
0.4154848456 1
 
0.2%
0.6227837801 1
 
0.2%
Other values (610) 610
97.4%
ValueCountFrequency (%)
0.159859091 1
0.2%
0.1654021144 1
0.2%
0.1735449433 1
0.2%
0.1855382025 1
0.2%
0.1890255213 1
0.2%
0.1923484653 1
0.2%
0.1941232234 1
0.2%
0.1949039996 1
0.2%
0.2054816931 1
0.2%
0.2169087827 1
0.2%
ValueCountFrequency (%)
1 5
0.8%
0.9994249344 1
 
0.2%
0.999263823 1
 
0.2%
0.9992578626 1
 
0.2%
0.9991359711 1
 
0.2%
0.9991348386 1
 
0.2%
0.9990851283 1
 
0.2%
0.9990175962 1
 
0.2%
0.9988521934 1
 
0.2%
0.9988068938 1
 
0.2%

country
Real number (ℝ)

Distinct129
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.466454
Minimum0
Maximum128
Zeros3
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-11-05T01:23:01.487190image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.25
Q134
median64.5
Q395
95-th percentile123
Maximum128
Range128
Interquartile range (IQR)61

Descriptive statistics

Standard deviation35.801269
Coefficient of variation (CV)0.55534727
Kurtosis-1.1051201
Mean64.466454
Median Absolute Deviation (MAD)30.5
Skewness0.009349683
Sum40356
Variance1281.7309
MonotonicityNot monotonic
2023-11-05T01:23:01.707253image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
123 14
 
2.2%
6 12
 
1.9%
108 11
 
1.8%
28 11
 
1.8%
91 11
 
1.8%
46 11
 
1.8%
95 11
 
1.8%
54 11
 
1.8%
68 10
 
1.6%
84 10
 
1.6%
Other values (119) 514
82.1%
ValueCountFrequency (%)
0 3
 
0.5%
1 4
 
0.6%
2 1
 
0.2%
3 4
 
0.6%
4 4
 
0.6%
5 2
 
0.3%
6 12
1.9%
7 2
 
0.3%
8 2
 
0.3%
9 7
1.1%
ValueCountFrequency (%)
128 8
1.3%
127 9
1.4%
126 1
 
0.2%
125 1
 
0.2%
124 4
 
0.6%
123 14
2.2%
122 4
 
0.6%
121 1
 
0.2%
120 3
 
0.5%
119 1
 
0.2%

year
Real number (ℝ)

Distinct34
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.0495
Minimum1981
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-11-05T01:23:01.910248image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1981
5-th percentile1992
Q12000
median2005
Q32011
95-th percentile2018
Maximum2020
Range39
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.8942475
Coefficient of variation (CV)0.0039371833
Kurtosis-0.79153557
Mean2005.0495
Median Absolute Deviation (MAD)6
Skewness-0.042348244
Sum1255161
Variance62.319144
MonotonicityNot monotonic
2023-11-05T01:23:02.278004image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
2000 59
 
9.4%
2005 42
 
6.7%
2006 38
 
6.1%
2010 31
 
5.0%
2011 25
 
4.0%
2014 25
 
4.0%
1998 24
 
3.8%
2012 24
 
3.8%
2019 23
 
3.7%
2001 23
 
3.7%
Other values (24) 312
49.8%
ValueCountFrequency (%)
1981 1
 
0.2%
1985 1
 
0.2%
1989 4
 
0.6%
1990 8
 
1.3%
1991 10
1.6%
1992 14
2.2%
1993 11
1.8%
1994 13
2.1%
1995 17
2.7%
1996 21
3.4%
ValueCountFrequency (%)
2020 2
 
0.3%
2019 23
3.7%
2018 22
3.5%
2017 9
 
1.4%
2016 13
2.1%
2015 16
2.6%
2014 25
4.0%
2013 16
2.6%
2012 24
3.8%
2011 25
4.0%

Interactions

2023-11-05T01:22:54.936720image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:37.013973image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:39.005675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:40.843630image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:42.579520image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:44.351346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:46.263462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:48.010449image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:49.670628image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:51.489266image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:53.179137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:55.111389image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:37.254637image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:39.174073image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:40.994919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:42.734817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:44.658102image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:46.418018image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:48.178236image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:49.828105image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:51.656326image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:53.342249image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:55.297358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:37.500720image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:39.331097image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:41.149284image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:42.900530image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:44.815386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:46.590820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:48.328542image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:50.129712image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:51.806087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:53.498938image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:55.631793image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:37.727487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:39.501293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:41.312426image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:43.059906image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:44.976822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:46.752620image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:48.489381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:50.285421image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:51.967125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:53.660177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:55.786186image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:37.895289image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:39.657554image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:41.466432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:43.232295image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:45.141198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:46.922037image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:48.651306image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:50.440150image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:52.119419image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:53.859666image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:55.961962image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:38.060547image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:39.922672image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:41.648017image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:43.398818image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:45.310380image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:47.090585image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:48.806987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:50.600523image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:52.274250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:54.018445image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:56.132732image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:38.235928image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:40.071996image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:41.806584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:43.571910image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:45.488137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:47.258397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:48.955669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:50.750036image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:52.421264image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:54.180691image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:56.278975image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:38.382946image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:40.226446image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:41.951783image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:43.713393image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:45.638350image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:47.408719image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:49.082111image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:50.881171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:52.573941image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:54.318881image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:56.412918image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:38.541301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:40.373271image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:42.093499image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:43.865962image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:45.791918image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:47.553396image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:49.226797image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:51.028164image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:52.710816image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:54.463984image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:56.567269image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:38.684954image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:40.515909image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:42.241570image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:44.011745image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:45.938948image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:47.693654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:49.366697image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:51.171811image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:52.850058image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:54.642431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:56.711679image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:38.848554image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:40.680408image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:42.401757image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:44.176915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:46.091632image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:47.848328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:49.507207image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:51.325421image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:53.009563image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T01:22:54.776373image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-05T01:23:02.449802image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Ages[15-49]_All_grade1Ages[15-49]_All_grade2Ages[15-49]_All_grade3Ages[15-49]_Male_grade1Ages[15-49]_Male_grade2Ages[15-49]_Male_grade3Ages[15-49]_Female_grade1Ages[15-49]_Female_grade2Ages[15-49]_Female_grade3countryyear
Ages[15-49]_All_grade11.0000.9930.9780.9800.9700.9520.9880.9840.9750.1410.180
Ages[15-49]_All_grade20.9931.0000.9940.9760.9820.9720.9790.9880.9870.1570.195
Ages[15-49]_All_grade30.9780.9941.0000.9650.9800.9830.9630.9790.9880.1680.211
Ages[15-49]_Male_grade10.9800.9760.9651.0000.9900.9720.9420.9400.9360.1610.168
Ages[15-49]_Male_grade20.9700.9820.9800.9901.0000.9920.9320.9430.9470.1760.186
Ages[15-49]_Male_grade30.9520.9720.9830.9720.9921.0000.9140.9330.9470.1820.203
Ages[15-49]_Female_grade10.9880.9790.9630.9420.9320.9141.0000.9940.9810.1190.185
Ages[15-49]_Female_grade20.9840.9880.9790.9400.9430.9330.9941.0000.9940.1340.200
Ages[15-49]_Female_grade30.9750.9870.9880.9360.9470.9470.9810.9941.0000.1490.216
country0.1410.1570.1680.1610.1760.1820.1190.1340.1491.0000.049
year0.1800.1950.2110.1680.1860.2030.1850.2000.2160.0491.000

Missing values

2023-11-05T01:22:56.947305image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-05T01:22:57.204717image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Ages[15-49]_All_grade1Ages[15-49]_All_grade2Ages[15-49]_All_grade3Ages[15-49]_Male_grade1Ages[15-49]_Male_grade2Ages[15-49]_Male_grade3Ages[15-49]_Female_grade1Ages[15-49]_Female_grade2Ages[15-49]_Female_grade3countryyear
00.6690910.6672140.6518120.8165460.8143550.8011920.5127330.5111900.49341402015
10.4858790.4801710.4655100.6158970.6107350.5943440.3372590.3309270.31824402007
20.5164310.5142330.5022690.6454720.6437220.6322240.3688000.3660900.35359202010
30.9142940.9035280.8716900.9437190.9345110.9017510.8870100.8747990.84381732015
40.8769900.8414970.7628640.9124380.8820790.8057880.8449080.8047690.72401731999
50.8895890.8520690.7719680.9303050.8957520.8259420.8530550.8128740.72353832008
60.8037330.7467890.6317200.8931830.8360060.7155780.7250150.6682740.55792232001
70.9810620.9810620.9804760.9779880.9779880.9767030.9836390.9836390.98363912002
80.9910790.9855560.9817430.9895500.9840750.9818270.9925720.9870030.98166112017
90.9924810.9900770.9844270.9873310.9858960.9774290.9978180.9944100.99167912000
Ages[15-49]_All_grade1Ages[15-49]_All_grade2Ages[15-49]_All_grade3Ages[15-49]_Male_grade1Ages[15-49]_Male_grade2Ages[15-49]_Male_grade3Ages[15-49]_Female_grade1Ages[15-49]_Female_grade2Ages[15-49]_Female_grade3countryyear
6160.9140650.8965300.8534330.9267770.9082160.8656010.9019220.8853660.8418101272002
6170.8964130.8788050.8474740.9100630.8953380.8629910.8813570.8605680.8303571271999
6180.9794000.9722520.9578790.9796180.9751490.9639880.9791640.9691120.9512551281994
6190.9820960.9768280.9655980.9853140.9811880.9658720.9784930.9719450.9652911281999
6200.9924800.9873970.9809780.9934360.9879980.9805940.9915580.9868180.9813481282005
6210.9876200.9852600.9788370.9874710.9840730.9766520.9877660.9864250.9809831282010
6220.9944780.9903480.9869150.9937740.9884100.9852920.9952030.9923420.9885841282015
6230.9936040.9894010.9834280.9922960.9880090.9802980.9949380.9908200.9866211282009
6240.9944250.9923000.9874990.9921690.9898580.9845510.9968910.9949710.9907221282014
6250.9936710.9918590.9851110.9923510.9909920.9825180.9951450.9928260.9880051282019

Duplicate rows

Most frequently occurring

Ages[15-49]_All_grade1Ages[15-49]_All_grade2Ages[15-49]_All_grade3Ages[15-49]_Male_grade1Ages[15-49]_Male_grade2Ages[15-49]_Male_grade3Ages[15-49]_Female_grade1Ages[15-49]_Female_grade2Ages[15-49]_Female_grade3countryyear# duplicates
00.9897980.982560.9743220.9883910.980430.9682110.9912210.9847120.98058819952